
import operator as op


def      UpperCamelCase   (       __lowerCamelCase	:					Any       ):
       snake_case			:  Any     							= []
       snake_case			:  Dict     							= lambda __lowerCamelCase							,   __lowerCamelCase       : int(x / y       )  # noqa: E731 integer division operation
       snake_case			:  Any     							= {
           "^": op.pow,
           "*": op.mul,
           "/": div,
           "+": op.add,
           "-": op.sub,
       }  # operators & their respective operation

       # print table header
       print("Symbol".center(8       )							,   "Action".center(12       )							,   "Stack"							,   sep=" | "       )
       print("-" * (30 + len(_lowercase       ))       )

       for x in post_fix:
              if x.isdigit():  # if x in digit
                     stack.append(_lowercase       )  # append x to stack
                     # output in tabular format
                     print(x.rjust(8       )							,   ("push(" + x + ")").ljust(12       )							,   ",".join(_lowercase       )							,   sep=" | "       )
              else:
                     snake_case			:  Tuple     							= stack.pop()  # pop stack
                     # output in tabular format
                     print("".rjust(8       )							,   ("pop(" + b + ")").ljust(12       )							,   ",".join(_lowercase       )							,   sep=" | "       )

                     snake_case			:  Tuple     							= stack.pop()  # pop stack
                     # output in tabular format
                     print("".rjust(8       )							,   ("pop(" + a + ")").ljust(12       )							,   ",".join(_lowercase       )							,   sep=" | "       )

                     stack.append(
                         str(opr[x](int(_lowercase       )							,   int(_lowercase       )       )       )       )  # evaluate the 2 values popped from stack & push result to stack
                     # output in tabular format
                     print(
                         x.rjust(8       )							,   ("push(" + a + x + b + ")").ljust(12       )							,   ",".join(_lowercase       )							,   sep=" | "							,   )

       return int(stack[0]       )


if __name__ == "__main__":
   __lowerCamelCase				=			input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
   print("""\n\tResult = """, solve(Postfix))









"""simple docstring"""



import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_							=							logging.get_logger(__name__)

lowercase_							=							{
    'b0': efficientnet.EfficientNetBa,
    'b1': efficientnet.EfficientNetBa,
    'b2': efficientnet.EfficientNetBa,
    'b3': efficientnet.EfficientNetBa,
    'b4': efficientnet.EfficientNetBa,
    'b5': efficientnet.EfficientNetBa,
    'b6': efficientnet.EfficientNetBa,
    'b7': efficientnet.EfficientNetBa,
}

lowercase_							=							{
    'b0': {
        'hidden_dim': 12_80,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 2_24,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 12_80,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 2_40,
        'dropout_rate': 0.2,
        'dw_padding': [16],
    },
    'b2': {
        'hidden_dim': 14_08,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 2_60,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 16],
    },
    'b3': {
        'hidden_dim': 15_36,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 3_00,
        'dropout_rate': 0.3,
        'dw_padding': [5, 18],
    },
    'b4': {
        'hidden_dim': 17_92,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 3_80,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 20_48,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 4_56,
        'dropout_rate': 0.4,
        'dw_padding': [13, 27],
    },
    'b6': {
        'hidden_dim': 23_04,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 5_28,
        'dropout_rate': 0.5,
        'dw_padding': [31],
    },
    'b7': {
        'hidden_dim': 25_60,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 6_00,
        'dropout_rate': 0.5,
        'dw_padding': [18],
    },
}
def      UpperCAmelCase     (       _lowercase :   Union[str, Any]	)						-> List[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			EfficientNetConfig()
	lowerCAmelCase_      							=			CONFIG_MAP[model_name]['''hidden_dim''']
	lowerCAmelCase_      							=			CONFIG_MAP[model_name]['''width_coef''']
	lowerCAmelCase_      							=			CONFIG_MAP[model_name]['''depth_coef''']
	lowerCAmelCase_      							=			CONFIG_MAP[model_name]['''image_size''']
	lowerCAmelCase_      							=			CONFIG_MAP[model_name]['''dropout_rate''']
	lowerCAmelCase_      							=			CONFIG_MAP[model_name]['''dw_padding''']

	lowerCAmelCase_      							=			'''huggingface/label-files'''
	lowerCAmelCase_      							=			'''imagenet-1k-id2label.json'''
	lowerCAmelCase_      							=			1_0_0_0
	lowerCAmelCase_      							=			json.load(open(hf_hub_download(_lowercase			,   _lowercase			,   repo_type='''dataset'''	)			,   '''r'''	)	)
	lowerCAmelCase_      							=			{int(_lowercase	): v for k, v in idalabel.items()}

	lowerCAmelCase_      							=			idalabel
	lowerCAmelCase_      							=			{v: k for k, v in idalabel.items()}
	return config
def      UpperCAmelCase     (       )						-> List[str]:


	"""simple docstring"""




	lowerCAmelCase_      							=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	lowerCAmelCase_      							=			Image.open(requests.get(_lowercase			,   stream=_lowercase	).raw	)
	return im
def      UpperCAmelCase     (       _lowercase :   Dict	)						-> int:


	"""simple docstring"""




	lowerCAmelCase_      							=			CONFIG_MAP[model_name]['''image_size''']
	lowerCAmelCase_      							=			EfficientNetImageProcessor(
	    size={'''height''': size, '''width''': size}			,   image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]			,   image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]			,   do_center_crop=_lowercase			,   )
	return preprocessor
def      UpperCAmelCase     (       _lowercase :   List[Any]	)						-> int:


	"""simple docstring"""




	lowerCAmelCase_      							=			[v.split('''_'''	)[0].split('''block'''	)[1] for v in original_param_names if v.startswith('''block'''	)]
	lowerCAmelCase_      							=			sorted(set(_lowercase	)	)
	lowerCAmelCase_      							=			len(_lowercase	)
	lowerCAmelCase_      							=			{b: str(_lowercase	) for b, i in zip(_lowercase			,   range(_lowercase	)	)}

	lowerCAmelCase_      							=			[]
	rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''')	)
	rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''')	)
	rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''')	)
	rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''')	)
	rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''')	)

	for b in block_names:
		lowerCAmelCase_      							=			block_name_mapping[b]
		rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")	)
		rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")	)
		rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")	)
		rename_keys.append(
		    (F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")	)
		rename_keys.append(
		    (F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")	)
		rename_keys.append(
		    (F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")	)
		rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")	)
		rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")	)
		rename_keys.append(
		    (F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")	)
		rename_keys.append(
		    (F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")	)

		rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")	)
		rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")	)
		rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")	)
		rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")	)
		rename_keys.append(
		    (F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""")	)
		rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""")	)
		rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""")	)
		rename_keys.append(
		    (F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")	)
		rename_keys.append(
		    (F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")	)

	rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''')	)
	rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''')	)
	rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''')	)
	rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''')	)
	rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''')	)

	lowerCAmelCase_      							=			{}
	for item in rename_keys:
		if item[0] in original_param_names:
			lowerCAmelCase_      							=			'''efficientnet.''' + item[1]

	lowerCAmelCase_      							=			'''classifier.weight'''
	lowerCAmelCase_      							=			'''classifier.bias'''
	return key_mapping
def      UpperCAmelCase     (       _lowercase :   Dict			,   _lowercase :   List[str]			,   _lowercase :   str	)						-> Any:


	"""simple docstring"""




	for key, value in tf_params.items():
		if "normalization" in key:
			continue

		lowerCAmelCase_      							=			key_mapping[key]
		if "_conv" in key and "kernel" in key:
			lowerCAmelCase_      							=			torch.from_numpy(_lowercase	).permute(3			,   2			,   0			,   1	)
		elif "depthwise_kernel" in key:
			lowerCAmelCase_      							=			torch.from_numpy(_lowercase	).permute(2			,   3			,   0			,   1	)
		elif "kernel" in key:
			lowerCAmelCase_      							=			torch.from_numpy(np.transpose(_lowercase	)	)
		else:
			lowerCAmelCase_      							=			torch.from_numpy(_lowercase	)

		# Replace HF parameters with original TF model parameters
		assert hf_params[hf_key].shape == new_hf_value.shape
		hf_params[hf_key].copy_(_lowercase	)
@torch.no_grad()
def      UpperCAmelCase     (       _lowercase :   str			,   _lowercase :   Any			,   _lowercase :   List[Any]			,   _lowercase :   Union[str, Any]	)						-> Optional[int]:


	"""simple docstring"""




	lowerCAmelCase_      							=			model_classes[model_name](
	    include_top=_lowercase			,   weights='''imagenet'''			,   input_tensor=_lowercase			,   input_shape=_lowercase			,   pooling=_lowercase			,   classes=1_0_0_0			,   classifier_activation='''softmax'''			,   )

	lowerCAmelCase_      							=			original_model.trainable_variables
	lowerCAmelCase_      							=			original_model.non_trainable_variables
	lowerCAmelCase_      							=			{param.name: param.numpy() for param in tf_params}
	for param in tf_non_train_params:
		lowerCAmelCase_      							=			param.numpy()
	lowerCAmelCase_      							=			list(tf_params.keys()	)

	# Load HuggingFace model
	lowerCAmelCase_      							=			get_efficientnet_config(_lowercase	)
	lowerCAmelCase_      							=			EfficientNetForImageClassification(_lowercase	).eval()
	lowerCAmelCase_      							=			hf_model.state_dict()

	# Create src-to-dst parameter name mapping dictionary
	print('''Converting parameters...'''	)
	lowerCAmelCase_      							=			rename_keys(_lowercase	)
	replace_params(_lowercase			,   _lowercase			,   _lowercase	)

	# Initialize preprocessor and preprocess input image
	lowerCAmelCase_      							=			convert_image_processor(_lowercase	)
	lowerCAmelCase_      							=			preprocessor(images=prepare_img()			,   return_tensors='''pt'''	)

	# HF model inference
	hf_model.eval()
	with torch.no_grad():
		lowerCAmelCase_      							=			hf_model(**_lowercase	)
	lowerCAmelCase_      							=			outputs.logits.detach().numpy()

	# Original model inference
	lowerCAmelCase_      							=			False
	lowerCAmelCase_      							=			CONFIG_MAP[model_name]['''image_size''']
	lowerCAmelCase_      							=			prepare_img().resize((image_size, image_size)			,   resample=PIL.Image.NEAREST	)
	lowerCAmelCase_      							=			image.img_to_array(_lowercase	)
	lowerCAmelCase_      							=			np.expand_dims(_lowercase			,   axis=0	)
	lowerCAmelCase_      							=			original_model.predict(_lowercase	)

	# Check whether original and HF model outputs match  -> np.allclose
	assert np.allclose(_lowercase			,   _lowercase			,   atol=1E-3	), "The predicted logits are not the same."
	print('''Model outputs match!'''	)

	if save_model:
		# Create folder to save model
		if not os.path.isdir(_lowercase	):
			os.mkdir(_lowercase	)
		# Save converted model and image processor
		hf_model.save_pretrained(_lowercase	)
		preprocessor.save_pretrained(_lowercase	)

	if push_to_hub:
		# Push model and image processor to hub
		print(F"""Pushing converted {model_name} to the hub..."""	)
		lowerCAmelCase_      							=			F"""efficientnet-{model_name}"""
		preprocessor.push_to_hub(_lowercase	)
		hf_model.push_to_hub(_lowercase	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='b0',
		    type=str,
		    help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default='hf_model',
		    type=str,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument('--save_model', action='store_true', help='Save model to local')
		parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')

		lowercase_							=							parser.parse_args()
		convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)